Detection of Digital Facial Retouching utilizing Face Beauty Information
This addresses a security issue in biometric systems where retouched images can compromise face recognition, though it is an incremental improvement over prior detection methods.
The paper tackled the problem of detecting digitally retouched faces, which can degrade face recognition systems, by analyzing changes in beauty assessment algorithms and evaluating whether face beauty information can improve detection. It achieved a detection rate of 1.1% D-EER in scenarios with unknown retouching algorithms.
Facial retouching to beautify images is widely spread in social media, advertisements, and it is even applied in professional photo studios to let individuals appear younger, remove wrinkles and skin impurities. Generally speaking, this is done to enhance beauty. This is not a problem itself, but when retouched images are used as biometric samples and enrolled in a biometric system, it is one. Since previous work has proven facial retouching to be a challenge for face recognition systems,the detection of facial retouching becomes increasingly necessary. This work proposes to study and analyze changes in beauty assessment algorithms of retouched images, assesses different feature extraction methods based on artificial intelligence in order to improve retouching detection, and evaluates whether face beauty can be exploited to enhance the detection rate. In a scenario where the attacking retouching algorithm is unknown, this work achieved 1.1% D-EER on single image detection.